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Information Retrieval Models
Published in Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar, Hybrid Intelligent Systems for Information Retrieval, 2023
Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar
Information retrieval (IR) methods such as cluster-based information retrieval organize, extract features, and categorize web content based on their similarity. Cluster-based IR, unlike previous techniques, is quick at processing big datasets of documents. To improve the quality of returned documents, increase the efficiency of IR, and limit the number of documents that are unrelated to the user’s search, the study “Cluster-Based Information Retrieval by Using (K-means)-Hierarchical Parallel Genetic Algorithms Approach” offers a (k-means)—Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the k-means clustering method with a hybrid PG of multi-deme and master/slave.
Towards application of linear genetic programming for indirect estimation of the resilient modulus of pavements subgrade soils
Published in Road Materials and Pavement Design, 2018
Ehsan Sadrossadat, Ali Heidaripanah, Behnam Ghorbani
The success of GP and its variants usually depends on increasing the initial and maximum program size parameter. One of the major problems happens in GP is “bloat” that is the tendency of GP individuals to grow in size without increasing in quality. This results in taking a lot of memory and slows down the evolutionary process. To avoid bloating, LGP uses variable-size chromosomes that are limited to a maximum number of instructions (Oltean & Grosan, 2003). Another major feature of LGP over GP is the usage of demes. Demes are semi-isolated subpopulations that evolution proceeds faster in them in comparison to a single population of equal size (Brameier & Banzhaf, 2007). To evaluate the fitness of the evolved program, the average of the squared raw errors is used. The resulting run is then accepted as the production run. The Discipulus software was chosen for establishment of LGP-based models (Conrads, Dolezal, Francone, & Nordin, 2004). Consequently, the model is selected on the basis of evaluating criteria for measuring performance of models as indicated before.